
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
from tqdm import tqdm
import re

# For counting frames with correctly identified chessboard pattern/frames used for calibration
count = 1

# Extracts the camera id from the image names
def get_cam_id(img_name):
    return re.search(r"(\d+)$", img_name).group()


# Path to the folder containing the images
image_folder = 'camera data\\'

pattern_wise_object_points = {}

# Chessboard square size (in mm) can be used if the chessboard square size is known
# square_size = 25

# Image format
image_format = '.png'

# initializing combinations of pattern sizes for searching the optimal chessboard size to detect the chessboard pattern 
# patterns are in descending order so that the bigger patterns are searched first to avoid detecting 
# intermediate smaller chessboard patterns i.e. avoiding detecting 3x3 pattern within 8x8 chessboard 
for i in range(8, 3, -1):
    for j in range(8, 3, -1):
        pattern_size = (i, j)
        
        # Prepare object points (0,0,0), (1,0,0), (2,0,0) ...., (9,6,0)
        objp = np.zeros((pattern_size[0]*pattern_size[1], 3), np.float32)
        objp[:, :2] = np.mgrid[0:pattern_size[0], 0:pattern_size[1]].T.reshape(-1, 2)
        # objp *= square_size
        
        # storing pattern size wise object points
        pattern_wise_object_points[pattern_size] = objp


# Get list of images in the folder
images = glob.glob(image_folder + '*' + image_format)

# intializing the dictionary for storing the calibration parametes per camera
camera_dict = {i:{"object_points": [], "image_points": []} for i in set([get_cam_id(img.split('.')[0]) for img in images]) }

# Loop through each image in the folder, tqdm used for creating progress bar
for fname in tqdm(images):
    # Extracting the camera id from image name
    cam_id = get_cam_id(fname.split('.')[0])
    
    # Read the image
    img = cv2.imread(fname)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
    # searching chessboard pattern in the current frame
    for k, v in pattern_wise_object_points.items():
        
        # Find the chessboard corners
        ret, corners = cv2.findChessboardCornersSB(gray, k, None)
        
        # If corners are found, add object points and image points against camera_id in the dictionary
        if ret:
            print('corners found!', k)
            
            # Updating the object points in camera dictionary
            temp_ojbect_points = camera_dict[cam_id]["object_points"]
            temp_ojbect_points.append(pattern_wise_object_points[k])
            camera_dict[cam_id]["object_points"] = temp_ojbect_points

            # Updating the image points in camera dictionary
            temp_image_points = camera_dict[cam_id]["image_points"]
            temp_image_points.append(corners)
            camera_dict[cam_id]["image_points"] = temp_image_points

            # Visualize and show corners
            img_corners = cv2.drawChessboardCorners(img, k, corners, ret)
            plt.imshow(cv2.cvtColor(img_corners, cv2.COLOR_BGR2RGB))
            plt.title(f'Chessboard Corners image no. {count}')
            count += 1
            plt.show()
            # breaking since the chessboard is detected
            break

  0%|                                                                                           | 0/97 [00:00<?, ?it/s]

corners found! (7, 7)

  2%|█▋                                                                                 | 2/97 [00:05<04:26,  2.81s/it]

corners found! (7, 7)

  4%|███▍                                                                               | 4/97 [00:09<03:24,  2.20s/it]

corners found! (7, 7)

  6%|█████▏                                                                             | 6/97 [00:13<03:12,  2.12s/it]

corners found! (7, 7)

  8%|██████▊                                                                            | 8/97 [00:18<03:45,  2.53s/it]

corners found! (7, 7)


# calibrating all cameras 
for k, v in camera_dict.items():
    print('camera id', k)
    # Calibrate camera
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(v['object_points'], v['image_points'], gray.shape[::-1], None, None)
    
    # storing calibrated camera parameters in the dictionary against camera_id
    camera_dict[k]['camera_matrix'] = mtx
    camera_dict[k]['distortion_coefficients'] = dist
    # Print the intrinsic parameters of the camera
    print("Intrinsic Parameters (Camera Matrix):")
    print(mtx)
    print("\nDistortion Coefficients:")
    print(dist)

camera id 8
Intrinsic Parameters (Camera Matrix):
[[2.01226284e+03 0.00000000e+00 8.01532842e+02]
 [0.00000000e+00 1.95211779e+03 3.75133979e+02]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00]]

Distortion Coefficients:
[[ 1.47409711e+00 -2.57604182e+01  5.79125640e-03 -1.13321127e-01
   2.38245407e+02]]
camera id 10
Intrinsic Parameters (Camera Matrix):
[[1.82508231e+04 0.00000000e+00 6.39578331e+02]
 [0.00000000e+00 1.82759528e+04 3.58947813e+02]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00]]

Distortion Coefficients:
[[ 2.12263462e-01 -1.12365892e+03  6.60530850e-02 -8.24250361e-03
  -1.52105505e+00]]
camera id 9
Intrinsic Parameters (Camera Matrix):
[[1.31261953e+04 0.00000000e+00 6.37653704e+02]
 [0.00000000e+00 1.29957836e+04 3.59145520e+02]
 [0.00000000e+00 0.00000000e+00 1.00000000e+00]]

Distortion Coefficients:
[[-4.70966847e+00  2.55999626e+03  3.41920036e-02  1.33462427e-02
   5.89385124e+00]]

Part 1:¶

Camera Calibration¶

Objective:¶

Assignment Details:¶

1. Dataset Description:¶

2. Task:¶

3. Submission Requirements:¶

Camera calibration¶

Potential challenges current in dataset¶

Solution¶

Part 2:¶

Multi-Camera System and 3D Trajectory Estimation¶

Objective:¶

Assignment Details:¶

1. 3D Coordinate Estimation:¶

2. Camera Pose Estimation:¶

3. 3D Trajectory Estimation:¶

4. Submission Requirements:¶

Solution¶

3D Coordinate Estimation:¶

3. 3D Trajectory Estimation:¶

Camera Pose Estimation:¶

Part 3:¶

Small Object Detection¶

Detecting ball in a camera frame Algorithm/Architecture:¶

solution¶

1. model architecture:¶

2. dataset¶

3. network modifications¶

4. Optimization and Hardware Acceleration:¶

Part 4:¶

Camera Specifications¶

Objective:¶

Assignment Details:¶

1. FPS and Shutter Speed:¶

2. Camera Synchronization Precision:¶

3. Submission Requirements:¶

Solution¶

FPS calculations¶

FPS required minimum 900 (considering 5 cm as acceptable ball movement per frame)¶

shutter speed¶

camera synchronisation¶